Between-sample variability from 16S rRna-gene sequencing data.
Clustering by timepoint (PCA):
Clustering by treatment (PCA)
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 7. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 7. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 8 rows containing missing values (geom_point).
NMDS1 vs NMDS2
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 7. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 8 rows containing missing values (geom_point).
Clustering by type (MDS): NMDS1 vs NMDS3
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 7. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 8 rows containing missing values (geom_point).
Clustering by type (MDS): NMDS2 vs NMDS3
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 7. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 7. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 7. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 8 rows containing missing values (geom_point).
Significance values based on permuted analysis of variance (999 permutations), repeated 100 times.
Below the ANOVA table from the model:
\[ X = \mu + treatment + timepoint + treatment*type + e \]
| Df | SumsOfSqs | MeanSqs | F.Model | R2 | Pr(>F) | |
|---|---|---|---|---|---|---|
| treatment | 6 | 0.6626034 | 0.1104339 | 1.020722 | 0.1197929 | 0.417 |
| residuals | 45 | 4.8686352 | 0.1081919 | NA | 0.8802071 | NA |
| total | 51 | 5.5312386 | NA | NA | 1.0000000 | NA |
## Run 0 stress 0.04298657
## Run 1 stress 0.04235479
## ... New best solution
## ... Procrustes: rmse 0.01071419 max resid 0.03832133
## Run 2 stress 0.04369622
## Run 3 stress 0.04373229
## Run 4 stress 0.04366829
## Run 5 stress 0.04241816
## ... Procrustes: rmse 0.006091945 max resid 0.03044901
## Run 6 stress 0.04235371
## ... New best solution
## ... Procrustes: rmse 0.01041482 max resid 0.05778179
## Run 7 stress 0.04236088
## ... Procrustes: rmse 0.01017229 max resid 0.05697562
## Run 8 stress 0.04240378
## ... Procrustes: rmse 0.007020887 max resid 0.03974594
## Run 9 stress 0.04364633
## Run 10 stress 0.04560874
## Run 11 stress 0.04356367
## Run 12 stress 0.04365055
## Run 13 stress 0.05413756
## Run 14 stress 0.04921116
## Run 15 stress 0.04243655
## ... Procrustes: rmse 0.007022739 max resid 0.03948807
## Run 16 stress 0.0423677
## ... Procrustes: rmse 0.01022542 max resid 0.05746601
## Run 17 stress 0.04249901
## ... Procrustes: rmse 0.01121507 max resid 0.06154593
## Run 18 stress 0.04927696
## Run 19 stress 0.04560166
## Run 20 stress 0.04918369
## *** No convergence -- monoMDS stopping criteria:
## 19: no. of iterations >= maxit
## 1: stress ratio > sratmax